Methods and Systems for Generating Integrated Substrate Maps for Cardiac Arrhythmias

ABSTRACT

An electrophysiology map, for example a map of arrhythmic substrate, can be generated by acquiring both geometry information and electrophysiology information pertaining to an anatomical region, and associating the acquired geometry and electrophysiology information as a plurality of electrophysiology data points. A user can select two (or more) electrophysiological characteristics for display, and can further elect to apply various filters to the selected electrophysiological characteristics. The user can also define various relationships (e.g., Boolean ANDs, ORs, and the like) between the selected and/or filtered characteristics. The user-selected filtering criteria can be applied to the electrophysiology data points to output various subsets thereof. These subsets can then be graphically rendered using various combinations of colorscale, monochrome scale, and iconography, for example as a three-dimensional cardiac electrophysiology model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional application No.62/063,989, filed 15 Oct. 2014, which is hereby incorporated byreference as though fully set forth herein.

BACKGROUND

The instant disclosure relates to anatomical mapping. In particular, theinstant disclosure relates to systems, apparatuses, and methods forcreating electrophysiology maps, such as may be useful in cardiacdiagnostic and therapeutic procedures.

Two mainstay hypotheses of arrhythmia maintenance mechanisms are singlesource focus and circus movement reentry. However, the mechanisms andsubstrates that maintain atrial fibrillation can vary frompatient-to-patient. Thus, there is a need for apparatuses, systems, andmethods that facilitate the identification, classification, andcharacterization of multiple arrhythmic mechanisms and substrates.

BRIEF SUMMARY

Disclosed herein is a method of generating an electrophysiology map,including: acquiring geometry information pertaining to an anatomicalregion, the geometry information including position information for aplurality of points in the anatomical region; acquiringelectrophysiology information pertaining to the anatomical region, theelectrophysiology information including a plurality ofelectrophysiological characteristics of the anatomical region;associating the geometry information with the electrophysiologyinformation as a plurality of electrophysiology (“EP”) data points;accepting user input to select a first electrophysiologicalcharacteristic of the plurality of electrophysiological characteristicsand a first filtering criterion for the first electrophysiologicalcharacteristic; accepting user input to select a secondelectrophysiological characteristic of the plurality ofelectrophysiological characteristics and a second filtering criterionfor the second electrophysiological characteristic; applying the firstfiltering criterion and the second filtering criterion to the pluralityof EP data points; and outputting a subset of the plurality of EP datapoints satisfying both the first filtering criterion and the secondfiltering criterion. For example, the first electrophysiologicalcharacteristic can be cycle length mean and the secondelectrophysiological characteristic can be cycle length standarddeviation. Similarly, the first filtering criterion can be a band passfilter with a pass band from 110 ms to 290 ms (e.g., between about 150ms and about 250 ms), and the second filtering criterion can be a bandpass filter with a pass band from 1 ms to 30 ms.

In other embodiments, the first and second electrophysiologicalcharacteristics and their respective filtering criteria can be selectedfrom the following pairings of electrophysiological characteristics andfiltering criteria: fractionation index and a high pass filter;peak-to-peak voltage and a low pass filter; electrogram sharpness and ahigh pass filter; conduction velocity consistency index and a high passfilter; and conduction velocity and a low pass filter.

It is also contemplated that a three-dimensional graphicalrepresentation of the subset of the plurality of EP data points can beoutput. For example, a user can provide input to prioritize the firstelectrophysiological characteristic and the second electrophysiologicalcharacteristic, such that the graphical representation of the subset ofthe plurality of EP data points is rendered according to theprioritization of the first electrophysiological characteristic and thesecond electrophysiological characteristic (e.g., with higher priorityelectrophysiological characteristics drawn preferentially to lowerpriority electrophysiological characteristics at any given point on thegraphical representation).

Also disclosed herein is a method of generating an electrophysiologymap, including: acquiring geometry information pertaining to ananatomical region, the geometry information including positioninformation for a plurality of points in the anatomical region;acquiring electrophysiology information pertaining to the anatomicalregion, the electrophysiology information including a plurality ofelectrophysiological characteristics of the anatomical region;associating the geometry information with the electrophysiologyinformation as a plurality of electrophysiology (“EP”) data points;accepting user input to select a first electrophysiologicalcharacteristic of the plurality of electrophysiological characteristics,a first filtering criterion for the first electrophysiologicalcharacteristic, and a first priority for the first electrophysiologicalcharacteristic; applying the first filtering criterion to the pluralityof EP data points to output a first subset of the plurality of EP datapoints satisfying the first filtering criterion; accepting user input toselect a second electrophysiological characteristic of the plurality ofelectrophysiological characteristics, a second filtering criterion forthe second electrophysiological characteristic, and a second priorityfor the second electrophysiological characteristic; applying the secondfiltering criterion to the plurality of EP data points to output asecond subset of the plurality of EP data points satisfying the secondfiltering criterion; outputting a three-dimensional graphicalrepresentation of the first and second subsets of the plurality of EPdata points according to the first priority and the second priority. Forexample, outputting a three-dimensional graphical representation of thefirst and second subsets of the plurality of EP data points according tothe first priority and the second priority can include: rendering thegraphical representation of the first subset of the plurality of EP datapoints preferentially to the graphical representation of the secondsubset of the plurality of data points if the first priority is higherthan the second priority; and rendering the graphical representation ofthe second subset of the plurality of EP data points preferentially tothe graphical representation of the first subset of the plurality ofdata points if the second priority is higher than the first priority.The graphical representations of the first and second subsets of theplurality of EP data points can be rendered using various combinationsof colorscale, monochrome scale, and iconography.

It should also be understood that the teachings herein are not limitedto two subsets of the plurality of EP data points, and can be extendedto any number of electrophysiological characteristics and/or filters.Thus, for example, according to certain aspects of the disclosure, themethod can further include: accepting user input to select a thirdelectrophysiological characteristic of the plurality ofelectrophysiological characteristics, a third filtering criterion forthe third electrophysiological characteristic, and a third priority forthe third electrophysiological characteristic; applying the thirdfiltering criterion to the plurality of EP data points to output a thirdsubset of the plurality of EP data points satisfying the third filteringcriterion; and outputting a three-dimensional graphical representationof the third subset of the plurality of EP data points according to thethird priority. As with the first and second subsets of the plurality ofEP data points, the third subset of the plurality of EP data points canlikewise be output using various combinations of colorscale, monochromescale, and iconography (e.g., one of the graphical representations ofthe first, second, and third subsets of the plurality of EP data pointscan be output in colorscale while the other two of the graphicalrepresentations of the first, second, and third subsets are output inmonochrome scale).

According to yet another aspect disclosed herein, a system forgenerating an electrophysiology map includes: an electrophysiology datapoint processor configured to accept as input geometry information andelectrophysiology information pertaining to an anatomical region and toassociate the geometry information with the electrophysiologyinformation as a plurality of electrophysiology data points; a filteringprocessor configured to accept as input a user's selection of nelectrophysiological characteristics, wherein each of the n selectedelectrophysiological characteristics has an associated filteringcriterion and an associated priority, and to apply the filteringcriteria to their respective ones of the n selected electrophysiologicalcharacteristics; and a mapping processor configured to output athree-dimensional representation of the filtered n selectedelectrophysiological characteristics according to their respectivepriorities.

The foregoing and other aspects, features, details, utilities, andadvantages of the present invention will be apparent from reading thefollowing description and claims, and from reviewing the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of an electrophysiology system, such as may beused in an electrophysiology study.

FIG. 2 depicts an exemplary multi-electrode catheter used in anelectrophysiology study.

FIG. 3 is a flowchart of representative steps that can be followed tocreate an electrophysiology map.

FIGS. 4a through 4d illustrate the application of the EP data pointfiltering teachings herein to maps of cycle length mean and cycle lengthstandard deviation.

FIGS. 5a through 5c illustrate the application of the EP data pointfiltering and electrophysiology map combination teachings herein withreference to cycle length mean and cycle length standard deviation.

FIGS. 6a and 6b illustrate the application of the EP data pointfiltering teachings herein to a map of fractionation index.

FIGS. 7a and 7b illustrate the application of the EP data pointfiltering teachings herein to a map of conduction velocity.

FIGS. 8a and 8b illustrate the application of the EP data pointfiltering and electrophysiology map combination teachings herein to amap of the magnitude, direction, and consistency of local conductionvelocity.

FIGS. 9a and 9b illustrate the application of the display prioritizationteachings herein to a map of cycle length mean, cycle length standarddeviation, and peak-to-peak voltage.

FIG. 10 is another electrophysiology map that illustrates the displayprioritization teachings herein.

DETAILED DESCRIPTION

The present disclosure relates to the collection and storage of dataused to create anatomical maps and to the display of anatomical mapsfrom the data so collected and stored, and provides methods,apparatuses, and systems for the storage and display of anatomical maps.As used herein, the term “anatomical map” refers to a graphicalrepresentation of an anatomical region that includes both a geometricmodel of the anatomical region and biological information of theanatomical region. For example, the biological information can besuperimposed upon the geometric model. As used herein, the term“superimposed” means that the biological information is displayed overthe geometric model and can, in some embodiments, be incorporated intothe geometric model. In other embodiments, the biological information isnot part of the geometric model itself, but rather “hovers” as anoverlay upon the geometric model.

Electrophysiology maps, such as those that can be created using system 8described below, are one type of anatomical map. For purposes ofillustration, several exemplary embodiments will be described in detailherein in the context of a cardiac electrophysiology procedure,including the creation of cardiac electrophysiology maps. It iscontemplated, however, that the methods, apparatuses, and systemsdisclosed herein can be utilized in other contexts.

Electrophysiology maps are generally created from a plurality ofelectrophysiology (“EP”) data points, each of which includes bothelectrophysiology data (e.g., endocardial and/or epicardial electrograms(“EGMs”)) and location data (e.g., information regarding the location ofthe apparatus (e.g., catheter and/or catheter-mounted electrodes)collecting the electrophysiology data, allowing the electrophysiologyinformation to be associated with a particular location in space (thatis, allowing the electrophysiology information to be interpreted asindicative of electrical activity at a point on the patient's heart).Insofar as the ordinarily skilled artisan will be familiar with variousmodalities for the acquisition and processing of EP data points(including, for example, both contact and non-contactelectrophysiological mapping), as well as with various techniques thatcan be used to generate a graphical representation from the plurality ofEP data points, these aspects will only be described herein to theextent necessary to understand the present disclosure.

FIG. 1 shows a schematic diagram of an electrophysiology system 8 forconducting cardiac electrophysiology studies by navigating a cardiaccatheter and measuring electrical activity occurring in a heart 10 of apatient 11 and three-dimensionally mapping the electrical activityand/or information related to or representative of the electricalactivity so measured. System 8 can be used, for example, to create ananatomical model of the patient's heart 10 using one or more electrodes.System 8 can also be used to measure electrophysiology data, includingvarious morphological characteristics, at a plurality of points along acardiac surface and store the measured data in association with locationinformation for each measurement point at which the electrophysiologydata was measured, for example to create a diagnostic data map of thepatient's heart 10.

As one of ordinary skill in the art will recognize, and as will befurther described below, system 8 can determine the location, and insome aspects the orientation, of objects, typically within athree-dimensional space, and express those locations as positioninformation determined relative to at least one reference.

For simplicity of illustration, the patient 11 is depicted schematicallyas an oval. In the embodiment shown in FIG. 1, three sets of surfaceelectrodes (e.g., patch electrodes) are shown applied to a surface ofthe patient 11, defining three generally orthogonal axes, referred toherein as an x-axis, a y-axis, and a z-axis. In other embodiments theelectrodes could be positioned in other arrangements, for examplemultiple electrodes on a particular body surface. As a furtheralternative, the electrodes do not need to be on the body surface, butcould be positioned internally to the body or on an external frame.

In FIG. 1, the x-axis surface electrodes 12, 14 are applied to thepatient along a first axis, such as on the lateral sides of the thoraxregion of the patient (e.g., applied to the patient's skin underneatheach arm) and may be referred to as the Left and Right electrodes. They-axis electrodes 18, 19 are applied to the patient along a second axisgenerally orthogonal to the x-axis, such as along the inner thigh andneck regions of the patient, and may be referred to as the Left Leg andNeck electrodes. The z-axis electrodes 16, 22 are applied along a thirdaxis generally orthogonal to both the x-axis and the y-axis, such asalong the sternum and spine of the patient in the thorax region, and maybe referred to as the Chest and Back electrodes. The heart 10 liesbetween these pairs of surface electrodes 12/14, 18/19, and 16/22.

An additional surface reference electrode (e.g., a “belly patch”) 21provides a reference and/or ground electrode for the system 8. The bellypatch electrode 21 may be an alternative to a fixed intra-cardiacelectrode 31, described in further detail below. It should also beappreciated that, in addition, the patient 11 may have most or all ofthe conventional electrocardiogram (“ECG” or “EKG”) system leads inplace. In certain embodiments, for example, a standard set of 12 ECGleads may be utilized for sensing electrocardiograms on the patient'sheart 10. This ECG information is available to the system 8 (e.g., itcan be provided as input to computer system 20). Insofar as ECG leadsare well understood, and for the sake of clarity in the figures, onlyone lead 6 and its connection to computer system 20 is illustrated inFIG. 1.

A representative catheter 13 having at least one electrode 17 (e.g., adistal electrode) is also depicted in schematic fashion in FIG. 1. Thisrepresentative catheter electrode 17 can be referred to as a“measurement electrode” or a “roving electrode.” Typically, multipleelectrodes on catheter 13, or on multiple such catheters, will be used.In one embodiment, for example, system 8 may utilize sixty-fourelectrodes on twelve catheters disposed within the heart and/orvasculature of the patient.

In other embodiments, system 8 may utilize a single catheter thatincludes multiple (e.g., eight) splines, each of which in turn includesmultiple (e.g., eight) electrodes. Of course, these embodiments aremerely exemplary, and any number of electrodes and catheters may beused. Indeed, in some embodiments, a high density mapping catheter, suchas the EnSite™ Array™ non-contact mapping catheter of St. Jude Medical,Inc., can be utilized.

Likewise, it should be understood that catheter 13 (or multiple suchcatheters) are typically introduced into the heart and/or vasculature ofthe patient via one or more introducers and using familiar procedures.For purposes of this disclosure, a segment of an exemplarymulti-electrode catheter 13 is shown in FIG. 2. In FIG. 2, catheter 13extends into the left ventricle 50 of the patient's heart 10 through atransseptal sheath 35. The use of a transseptal approach to the leftventricle is well known and will be familiar to those of ordinary skillin the art, and need not be further described herein. Of course,catheter 13 can also be introduced into the heart 10 in any othersuitable manner.

Catheter 13 includes electrode 17 on its distal tip, as well as aplurality of additional measurement electrodes 52, 54, 56 spaced alongits length in the illustrated embodiment. Typically, the spacing betweenadjacent electrodes will be known, though it should be understood thatthe electrodes may not be evenly spaced along catheter 13 or of equalsize to each other. Since each of these electrodes 17, 52, 54, 56 lieswithin the patient, location data may be collected simultaneously foreach of the electrodes by system 8. Similarly, each of electrodes 17,52, 54, and 56 can be used to gather electrophysiological data from thecardiac surface.

Returning now to FIG. 1, in some embodiments, a fixed referenceelectrode 31 (e.g., attached to a wall of the heart 10) is shown on asecond catheter 29. For calibration purposes, this electrode 31 may bestationary (e.g., attached to or near the wall of the heart) or disposedin a fixed spatial relationship with the roving electrodes (e.g.,electrodes 17, 52, 54, 56), and thus may be referred to as a“navigational reference” or “local reference.” The fixed referenceelectrode 31 may be used in addition or alternatively to the surfacereference electrode 21 described above. In many instances, a coronarysinus electrode or other fixed electrode in the heart 10 can be used asa reference for measuring voltages and displacements; that is, asdescribed below, fixed reference electrode 31 may define the origin of acoordinate system.

Each surface electrode is coupled to a multiplex switch 24, and thepairs of surface electrodes are selected by software running on acomputer 20, which couples the surface electrodes to a signal generator25. Alternately, switch 24 may be eliminated and multiple (e.g., three)instances of signal generator 25 may be provided, one for eachmeasurement axis (that is, each surface electrode pairing).

The computer 20, for example, may comprise a conventionalgeneral-purpose computer, a special-purpose computer, a distributedcomputer, or any other type of computer. The computer 20 may compriseone or more processors 28, such as a single central processing unit(CPU), or a plurality of processing units, commonly referred to as aparallel processing environment, which may execute instructions topractice the various aspects disclosed herein.

Generally, three nominally orthogonal electric fields are generated by aseries of driven and sensed electric dipoles (e.g., surface electrodepairs 12/14, 18/19, and 16/22) in order to realize catheter navigationin a biological conductor. Alternatively, these orthogonal fields can bedecomposed and any pairs of surface electrodes can be driven as dipolesto provide effective electrode triangulation. Likewise, the electrodes12, 14, 18, 19, 16, and 22 (or any other number of electrodes) could bepositioned in any other effective arrangement for driving a current toor sensing a current from an electrode in the heart. For example,multiple electrodes could be placed on the back, sides, and/or belly ofpatient 11. For any desired axis, the potentials measured across theroving electrodes resulting from a predetermined set of drive(source-sink) configurations may be combined algebraically to yield thesame effective potential as would be obtained by simply driving auniform current along the orthogonal axes.

Thus, any two of the surface electrodes 12, 14, 16, 18, 19, 22 may beselected as a dipole source and drain with respect to a groundreference, such as belly patch 21, while the unexcited electrodesmeasure voltage with respect to the ground reference. The rovingelectrodes 17, 52, 54, 56 placed in the heart 10 are exposed to thefield from a current pulse and are measured with respect to ground, suchas belly patch 21. In practice the catheters within the heart 10 maycontain more or fewer electrodes than the four shown, and each electrodepotential may be measured. As previously noted, at least one electrodemay be fixed to the interior surface of the heart to form a fixedreference electrode 31, which is also measured with respect to ground,such as belly patch 21, and which may be defined as the origin of thecoordinate system relative to which localization system 8 measurespositions. Data sets from each of the surface electrodes, the internalelectrodes, and the virtual electrodes may all be used to determine thelocation of the roving electrodes 17, 52, 54, 56 within heart 10.

The measured voltages may be used by system 8 to determine the locationin three-dimensional space of the electrodes inside the heart, such asroving electrodes 17, 52, 54, 56, relative to a reference location, suchas reference electrode 31. That is, the voltages measured at referenceelectrode 31 may be used to define the origin of a coordinate system,while the voltages measured at roving electrodes 17, 52, 54, 56 may beused to express the location of roving electrodes 17, 52, 54, 56relative to the origin. In some embodiments, the coordinate system is athree-dimensional (x, y, z) Cartesian coordinate system, although othercoordinate systems, such as polar, spherical, and cylindrical coordinatesystems, are contemplated.

As should be clear from the foregoing discussion, the data used todetermine the location of the electrode(s) within the heart is measuredwhile the surface electrode pairs impress an electric field on theheart. The electrode data may also be used to create a respirationcompensation value used to improve the raw location data for theelectrode locations as described in U.S. Pat. No. 7,263,397, which ishereby incorporated herein by reference in its entirety. The electrodedata may also be used to compensate for changes in the impedance of thebody of the patient as described, for example, in U.S. Pat. No.7,885,707, which is also incorporated herein by reference in itsentirety.

In one representative embodiment, the system 8 first selects a set ofsurface electrodes and then drives them with current pulses. While thecurrent pulses are being delivered, electrical activity, such as thevoltages measured with at least one of the remaining surface electrodesand in vivo electrodes, is measured and stored. Compensation forartifacts, such as respiration and/or impedance shifting, may beperformed as indicated above.

In some embodiments, system 8 is the EnSite™ Velocity™ cardiac mappingand visualization system of St. Jude Medical, Inc., which generateselectrical fields as described above, or another such system that reliesupon electrical fields. Other systems, however, may be used inconnection with the present teachings, including for example, the CARTOnavigation and location system of Biosense Webster, Inc., the AURORA®system of Northern Digital Inc., or Sterotaxis' NIOBE® MagneticNavigation System, all of which utilize magnetic fields rather thanelectrical fields. The localization and mapping systems described in thefollowing patents (all of which are hereby incorporated by reference intheir entireties) can also be used with the present invention: U.S. Pat.Nos. 6,990,370; 6,978,168; 6,947,785; 6,939,309; 6,728,562; 6,640,119;5,983,126; and 5,697,377.

FIG. 3 is a flowchart of representative steps that can be carried out tocreate an electrophysiology map. Advantageously, the electrophysiologymaps disclosed herein can depict multiple variables (e.g., multiplecardiac electrophysiological characteristics) by location (e.g.,according to their position on the surface of the heart). In someembodiments, the flowchart may represent several exemplary steps thatcan be carried out by the computer 20 of FIG. 1 (e.g., by one or moreprocessors 28) to generate an electrophysiology map. It should beunderstood that the representative steps described below can be eitherhardware- or software-implemented. For the sake of explanation, the term“signal processor” is used herein to describe both hardware- andsoftware-based implementations of the teachings herein.

In block 302, geometry information pertaining to an anatomical region(e.g., a heart chamber) is acquired. The acquired geometry informationincludes position information (e.g., Cartesian coordinates) for aplurality of points in the anatomical region.

The geometry can be acquired in numerous ways, many of which will befamiliar to the person of ordinary skill in the art. For example, incertain aspects, system 8 is used to gather a plurality of locationpoints that define the geometry of the anatomical region; the pluralityof location points can then be used to create a model of the anatomicalregion. In other aspects, an external imaging modality, such as magneticresonance imaging (“MRI”), computed tomography (“CT”), positron emissiontomography (“PET”), ultrasound imaging, single-photon emission computedtomography (“SPECT”), or the like is used. It is also contemplated thatmultiple geometries can be acquired from multiple imaging modalities.Where multiple geometries are acquired, they can be fused or registeredto a common coordinate system, for example as disclosed in U.S.application Ser. No. 11/715,923, filed 9 Mar. 2007, and/or U.S.application Ser. No. 13/087,203, filed 14 Apr. 2011, both of which arehereby incorporated by reference as though fully set forth herein.

Further, the geometry information can be time-varying. For example,cardiac geometry will vary over time with the beating of the heart.Thus, rather than acquiring a single geometry at a single point in time(e.g., max systole or max diastole), a plurality of time-varyinggeometries can be captured, such as by segmenting multiple phases fromvolumetric images captured by an MRI or CT system. These time-varyinggeometries can be used to create an animated geometric model of theanatomical region.

In block 304, electrophysiology information pertaining to the anatomicalregion is acquired. The electrophysiology information includes aplurality of electrophysiological characteristics of the anatomicalregion. Just as there are numerous ways to acquire the geometryinformation in block 302, so too are there numerous ways to acquire theelectrophysiology information in block 304. For example, system 8 (e.g.,electrodes 17, 52, 54, and 56 on catheter 13) can be used to measureelectrical activity on the surface of the patient's heart 10. Theelectrophysiology information can also be time-varying.

In block 306, the geometry information acquired in block 302 isassociated with the electrophysiology information acquired in block 304.For example, electrophysiology measurements made by electrodes 17, 52,54, 56 can be associated with the position of catheter 13 at the timethe measurements were made. As another example, electrophysiologymeasurements can be associated with locations within a CT model afterthe CT model has been registered to the coordinate frame of system 8.This creates a plurality of electrophysiology data points that can thenbe stored, for example in the memory of computer system 20, for use inthe creation (and manipulation) of anatomical maps.

As mentioned above, the ordinarily skilled artisan will be familiar withnumerous electrophysiology maps. For example, maps of conductionvelocity and/or consistency index, such as disclosed in U.S. provisionalapplication No. 62/063,987, filed 15 Oct. 2014, which is herebyincorporated by reference as though fully set forth herein, are bothelectrophysiology maps. Other electrophysiology maps include, withoutlimitation, complex fractionated electrogram (“CFE”) maps (e.g., maps ofcycle length mean and cycle length standard deviation), fractionationindex maps, peak-to-peak voltage maps, lateness maps (e.g., Late-P andLate-A), local activation time (“LAT”) maps, and electrogram (“EGM”)sharpness maps.

It is known to show a single electrophysiological characteristic perelectrophysiology map. However, with the proliferation ofelectrophysiological characteristics that may be of interest to apractitioner during an electrophysiology study, aone-characteristic-per-map approach can become cumbersome.

Likewise, it is known to display a full range of values for anelectrophysiological characteristic on an electrophysiology map (e.g.,displaying peak-to-peak voltages from the lowest value measured withinthe anatomical region to the highest value measured within theanatomical region). This requires the presentation scale (e.g., colorscale, monochrome scale, or other graphical convention) to be spreadover a relatively large range. A practitioner may, however, beinterested only in a narrower range of values for theelectrophysiological characteristic. For example, U.S. patentapplication Ser. No. 14/504,174, filed 1 Oct. 2014 and herebyincorporated by reference as though fully set forth herein, describesthe use of upper and lower bounds on the use of a presentation scale forlateness attributes.

According to an aspect of the instant disclosure, multipleelectrophysiology characteristics can be presented on a singleelectrophysiology map. According to another aspect of the instantdisclosure, the user can set various filters (e.g., high-pass, low-pass,band-pass, band-reject) on the presentation of the electrophysiologicalcharacteristics, such that only certain ranges of values for givenelectrophysiological characteristics are displayed.

Thus, in steps 308 a, 308 b, respectively, a user can select a firstelectrophysiology characteristic (e.g., cycle length mean) and a secondelectrophysiology characteristic (e.g., cycle length standard deviation)to display in an electrophysiology map.

Similarly, in steps 310 a, 310 b, respectively, the user can set firstand second filter criteria corresponding, respectively, to the selectedfirst and second electrophysiology characteristics. For example, theuser can choose to apply a band-pass filter of 150 ms to 250 ms to theCFE cycle length mean and a band-pass filter of 1 ms to 30 ms to the CFEcycle length standard deviation.

The selected filters are applied to the plurality of EP data points inblock 312. In certain aspects, the output of the filters is a subset ofthe plurality of EP data points that meet both the filtering criteriaset in blocks 310 a, 310 b. That is, the filters can be applied to theplurality of EP data points with a Boolean AND. In other aspects, theoutput of the filters is a subset of the plurality of EP data pointsthat meet either of the filtering criteria set in blocks 310 a, 310 b.That is, the filters can be applied to the plurality of EP data pointswith a Boolean OR. It is also contemplated that the application of thefilters can yield two subsets: a first subset that satisfies the firstfiltering criterion of block 310 a and a second subset that satisfiesthe second filtering criterion of block 310 b.

An electrophysiology map is rendered in block 314. The electrophysiologymap is a graphical representation of the subset of the plurality of EPdata points that results from the application of the first and secondfilters in block 312. Thus, for example, the acquired geometry (orgeometries) can be rendered graphically (e.g., using techniques that arefamiliar to those of ordinary skill in the art), and a map of theelectrophysiology characteristics, post-filtering, can be superimposedthereon.

FIGS. 4a through 4d and 5a through 5c illustrate the foregoingteachings. FIG. 4a is a traditional cycle length mean map without afilter applied, while FIG. 4b applies a band-pass filter having a passband of 170 ms to 250 ms to the map of FIG. 4a . Similarly, FIG. 4c is atraditional cycle length standard deviation map without a filterapplied, while FIG. 4d applies a band-pass filter having a pass band of1 ms to 40 ms to the map of FIG. 4 c.

As can be seen by comparing FIGS. 4a and 4c , on the one hand, to FIGS.4b and 4d , on the other hand, only the regions having values ofinterest are mapped in FIGS. 4b and 4d . The same range of colorscalevalues is thus applied over a narrower numerical range in FIGS. 4b and4d than in FIGS. 4a and 4c , allowing a more refined presentation inFIGS. 4b and 4d relative to FIGS. 4a and 4c (that is, more subtlevariations in the depicted electrophysiological characteristic can bedepicted in FIGS. 4b and 4d than in FIGS. 4a and 4c ).

Turning now to FIGS. 5a through 5c , suppose that a practitioner isinterested in identifying regions of cardiac tissue that exhibit fastand regular activation. Such regions could, for example, be defined asregions that exhibit both a cycle length mean between 150 ms and 250 msand a cycle length standard deviation between 1 ms and 30 ms. FIG. 5a ,therefore, is an exemplary electrophysiology map that graphicallypresents only those EP data points meeting both criteria (i.e., EP datapoints having a cycle length mean between 150 ms and 250 ms and a cyclelength standard deviation between 1 ms and 30 ms). The advantages ofsuch a presentation are similar to those discussed above with respect toFIGS. 4a-4d . In particular, FIG. 5b illustrates the types of EP datapoints that are included in the map of FIG. 5a (that is, EP data pointsthat exhibit electrophysiological characteristics of interest to thepractitioner), while FIG. 5c illustrates the types of EP data pointsthat are excluded in the map of FIG. 5c (that is, EP data points that donot exhibit electrophysiological characteristics of interest to thepractitioner).

It should be understood that FIGS. 4a-4d and 5a-5c are merely exemplaryof the present teachings. In other embodiments, a user might select thefollowing electrophysiology characteristics and associated filters:

-   -   Using a high pass filter and a map of fractionation index to        identify areas with high fractionation, which may be critical        substrates for the perpetuation of atrial fibrillation, and thus        desirable targets for ablation to treat the same (compare FIG.        6a with FIG. 6b );    -   Using a low pass filter and a map of peak-to-peak voltage to        identify areas with low peak-to-peak voltage; areas of low        voltage can indicate fibrosis or scarring, which can serve as a        substrate for arrhythmia maintenance;    -   Using a high pass filter and a map of electrogram sharpness to        identify areas with high sharpness, which might be indicative of        an ectopic focus;    -   Using a high pass filter and a map of conduction velocity        consistency index to identify regions where the direction of the        propagating activation wavefront is highly consistent, which can        highlight a wavefront circuit in an atrial arrhythmia; and/or    -   Using a low pass filter and a map of conduction velocity to        identify regions of slow conduction; because slow conduction is        a hallmark of maintaining a reentrant circuit, the regions        identified by such a map can be targeted for ablation therapy in        the treatment of ventricular tachycardia (“VT”) (compare FIG. 7a        with FIG. 7b ).

Of course, even this list of embodiments is non-exhaustive, and theperson of ordinary skill in the art would readily appreciate how toextend the teachings herein to additional electrophysiology maps andcombinations of electrophysiology maps.

Similarly, various conventions can be used to present the variouscombinations of electrophysiology maps discussed herein. For example,various combinations of full color and monochrome scales (e.g., greyscale, brown scale) can be used in the display of electrophysiologymaps. Various iconography can also be used. For example, FIGS. 8a(unfiltered) and 8 b (high pass filtered for consistency index)illustrate the use of arrowheads to depict activation direction(direction of arrow), local conduction velocity (length of arrow), andconsistency index (width of arrow). In an alternative embodiment, azig-zag line can be used instead of an arrowhead to represent conductionvelocity, with a tighter zig-zag reflecting a lower conduction velocity.Still other methods of presenting electrophysiology maps are disclosedin U.S. provisional application No. 61/935,954, filed 7 Feb. 2014, whichis hereby incorporated by reference as though fully set forth herein.

Where multiple different electrophysiology characteristics are to bedisplayed on a single electrophysiology map, the user can assignpriorities to the various characteristics. Once the characteristics areprioritized, any regions of overlap can be rendered according to therelative priorities of the characteristics, with higher prioritycharacteristics drawn preferentially to lower priority characteristics.Stated another way, if a particular EP data point includes data formultiple electrophysiology characteristics that satisfy theuser-specified filters, the EP data point will be displayed with thehighest priority electrophysiology characteristic.

For example, FIG. 9a is similar to FIG. 5a in that it depicts a map ofEP data points that have been band pass filtered for cycle length meanand standard deviation. In FIG. 9b , a low pass filtered peak-to-peakmap is added to the map of FIG. 9a , with the peak-to-peak map having ahigher priority. Thus, where the two maps overlap (that is, where EPdata points satisfy both the cycle length and peak-to-peak filters), thepeak-to-peak map is drawn instead of the cycle length map (see region900).

FIG. 10 is another exemplary prioritized electrophysiology map. The mapof FIG. 10 includes as the highest priority a low pass filteredpeak-to-peak map, as the next highest priority a high pass filteredfractionation index map, and as the lowest priority a band pass filteredmap of cycle length mean and standard deviation as described above. FIG.10 also includes conduction velocity arrows that have been high passfiltered for consistency index and star icons indicative of focalactivity as identified using high pass filtered sharpness data.

Although several embodiments of this invention have been described abovewith a certain degree of particularity, those skilled in the art couldmake numerous alterations to the disclosed embodiments without departingfrom the spirit or scope of this invention.

For example, although the electrophysiology maps disclosed herein havebeen described in the context of a two-characteristic (and, similarly,two-filter) map, the ordinarily skilled artisan will appreciate how toextend the teachings herein to any n-characteristic and/or n-filter map,where n is an integer greater than 1.

As another example, although some of the filtering methods describedabove are applied with Boolean ANDs and Boolean ORs, the teachingsherein could be adapted to other relationships between characteristics(e.g., filtered characteristic one AND NOT (filtered characteristic twoOR filtered characteristic three)).

All directional references (e.g., upper, lower, upward, downward, left,right, leftward, rightward, top, bottom, above, below, vertical,horizontal, clockwise, and counterclockwise) are only used foridentification purposes to aid the reader's understanding of the presentinvention, and do not create limitations, particularly as to theposition, orientation, or use of the invention. Joinder references(e.g., attached, coupled, connected, and the like) are to be construedbroadly and may include intermediate members between a connection ofelements and relative movement between elements. As such, joinderreferences do not necessarily infer that two elements are directlyconnected and in fixed relation to each other.

It is intended that all matter contained in the above description orshown in the accompanying drawings shall be interpreted as illustrativeonly and not limiting. Changes in detail or structure may be madewithout departing from the spirit of the invention as defined in theappended claims.

What is claimed is:
 1. A method of generating an electrophysiology map,comprising: acquiring geometry information pertaining to an anatomicalregion, the geometry information comprising position information for aplurality of points in the anatomical region; acquiringelectrophysiology information pertaining to the anatomical region, theelectrophysiology information comprising a plurality ofelectrophysiological characteristics of the anatomical region;associating the geometry information with the electrophysiologyinformation as a plurality of electrophysiology (“EP”) data points;accepting user input to select a first electrophysiologicalcharacteristic of the plurality of electrophysiological characteristicsand a first filtering criterion for the first electrophysiologicalcharacteristic; accepting user input to select a secondelectrophysiological characteristic of the plurality ofelectrophysiological characteristics and a second filtering criterionfor the second electrophysiological characteristic; applying the firstfiltering criterion and the second filtering criterion to the pluralityof EP data points; and outputting a subset of the plurality of EP datapoints satisfying both the first filtering criterion and the secondfiltering criterion.
 2. The method according to claim 1, wherein thefirst electrophysiological characteristic comprises cycle length meanand the second electrophysiological characteristic comprises cyclelength standard deviation.
 3. The method according to claim 2, whereinthe first filtering criterion comprises a band pass filter with a passband from 110 ms to 290 ms and second filtering criterion comprises aband pass filter with a pass band from 1 ms to 30 ms.
 4. The methodaccording to claim 1, wherein the first electrophysiologicalcharacteristic and the first filtering criterion respectively compriseone of: fractionation index and a high pass filter; peak-to-peak voltageand a low pass filter; electrogram sharpness and a high pass filter;conduction velocity consistency index and a high pass filter; andconduction velocity and a low pass filter.
 5. The method according toclaim 4, wherein the second electrophysiological characteristic and thesecond filtering criterion respectively comprise one of: fractionationindex and a high pass filter; peak-to-peak voltage and a low passfilter; electrogram sharpness and a high pass filter; conductionvelocity consistency index and a high pass filter; and conductionvelocity and a low pass filter, and wherein the secondelectrophysiological characteristic and the second filtering criteriondiffer, respectively, from the first electrophysiological characteristicand the first filtering criterion.
 6. The method according to claim 1,further comprising outputting a three-dimensional graphicalrepresentation of the subset of the plurality of EP data points.
 7. Themethod according to claim 6, further comprising accepting user inputprioritizing the first electrophysiological characteristic and thesecond electrophysiological characteristic, wherein the graphicalrepresentation of the subset of the plurality of EP data points isrendered according to the prioritization of the firstelectrophysiological characteristic and the second electrophysiologicalcharacteristic.
 8. A method of generating an electrophysiology map,comprising: acquiring geometry information pertaining to an anatomicalregion, the geometry information comprising position information for aplurality of points in the anatomical region; acquiringelectrophysiology information pertaining to the anatomical region, theelectrophysiology information comprising a plurality ofelectrophysiological characteristics of the anatomical region;associating the geometry information with the electrophysiologyinformation as a plurality of electrophysiology (“EP”) data points;accepting user input to select a first electrophysiologicalcharacteristic of the plurality of electrophysiological characteristics,a first filtering criterion for the first electrophysiologicalcharacteristic, and a first priority for the first electrophysiologicalcharacteristic; applying the first filtering criterion to the pluralityof EP data points to output a first subset of the plurality of EP datapoints satisfying the first filtering criterion; accepting user input toselect a second electrophysiological characteristic of the plurality ofelectrophysiological characteristics, a second filtering criterion forthe second electrophysiological characteristic, and a second priorityfor the second electrophysiological characteristic; applying the secondfiltering criterion to the plurality of EP data points to output asecond subset of the plurality of EP data points satisfying the secondfiltering criterion; outputting a three-dimensional graphicalrepresentation of the first and second subsets of the plurality of EPdata points according to the first priority and the second priority. 9.The method according to claim 8, wherein outputting a three-dimensionalgraphical representation of the first and second subsets of theplurality of EP data points according to the first priority and thesecond priority comprises: rendering the graphical representation of thefirst subset of the plurality of EP data points preferentially to thegraphical representation of the second subset of the plurality of datapoints if the first priority is higher than the second priority; andrendering the graphical representation of the second subset of theplurality of EP data points preferentially to the graphicalrepresentation of the first subset of the plurality of data points ifthe second priority is higher than the first priority.
 10. The methodaccording to claim 8, wherein outputting a three-dimensional graphicalrepresentation of the first and second subsets of the plurality of EPdata points according to the first priority and the second prioritycomprises: rendering the graphical representation of the first subset ofthe plurality of data points using a colorscale; and rendering thegraphical representation of the second subset of the plurality of datapoints using a monochrome scale.
 11. The method according to claim 8,wherein outputting a three-dimensional graphical representation of thefirst and second subsets of the plurality of EP data points according tothe first priority and the second priority comprises: rendering thegraphical representation of the first subset of the plurality of datapoints using one of a colorscale and a monochrome scale; and renderingthe graphical representation of the second subset of the plurality ofdata points using iconography.
 12. The method according to claim 8,further comprising: accepting user input to select a thirdelectrophysiological characteristic of the plurality ofelectrophysiological characteristics, a third filtering criterion forthe third electrophysiological characteristic, and a third priority forthe third electrophysiological characteristic; applying the thirdfiltering criterion to the plurality of EP data points to output a thirdsubset of the plurality of EP data points satisfying the third filteringcriterion; and outputting a three-dimensional graphical representationof the third subset of the plurality of EP data points according to thethird priority.
 13. The method according to claim 12, wherein one of thegraphical representations of the first, second, and third subsets of theplurality of EP data points is output in colorscale and two of thegraphical representations of the first, second, and third subsets isoutput in monochrome scale.
 14. A system for generating anelectrophysiology map, comprising: an electrophysiology data pointprocessor configured to accept as input geometry information andelectrophysiology information pertaining to an anatomical region and toassociate the geometry information with the electrophysiologyinformation as a plurality of electrophysiology data points; a filteringprocessor configured to accept as input a user's selection of nelectrophysiological characteristics, wherein each of the n selectedelectrophysiological characteristics has an associated filteringcriterion and an associated priority, and to apply the filteringcriteria to their respective ones of the n selected electrophysiologicalcharacteristics; and a mapping processor configured to output athree-dimensional representation of the filtered n selectedelectrophysiological characteristics according to their respectivepriorities.